Brain tissue classification

ABSTRACT

A medical imaging processing method includes: using an imaging method to acquire at least first and second data sets of a region of interest of a patient&#39;s body, with at least one image acquisition parameter being changed so that first and second data sets yield different contrast levels relating to different substance and/or tissue types, and wherein the at least one acquisition parameter used to obtain the first data set is selected to enhance the contrast between one of the substance and/or tissue types relative to other substance and/or tissue types, and the at least one acquisition parameter used to obtain the second data set is selected to enhance the contrast between a different one of the substance and/or tissue types relative other substance and/or tissue types, thereby to optimize the contrast between at least three different substance and/or tissue types; and processing the two data sets to identify the different tissue types and/or boundaries therebetween.

RELATED APPLICATION DATA

This application claims priority of U.S. Provisional Application No.60/724,231 filed on Oct. 6, 2005, which is incorporated herein byreference in its entirety.

FIELD OF THE INVENTION

The present invention relates to image processing for surfacedelineation and, more particularly, to a method and apparatus fordetecting boundaries between brain tissue and cerebrospinal fluid (CSF)filled regions to classify brain tissue.

BACKGROUND OF THE INVENTION

Tissue-CSF boundaries between brain tissue and cerebrospinal fluid (CSF)occur primarily at an outer cortical surface and in interior regions ofthe brain at CSF-filled ventricles. Locating these surfaces isbeneficial for some neurosurgical procedures, such asconvection-enhanced drug delivery, since placement of infusion catheterstoo close to CSF boundaries is known to lead to poor dispersion ofinfusate in tissue, as the infusate flows into the less-resistiveCSF-filled regions.

Large CSF-filled structures are easily distinguished from brain tissuein many MRI imaging sequences. At the cortical surface, however, thinsulci are often narrower than the resolution provided by MR imagery and,thus, are not reliably detected. Furthermore, the problem can becomplicated by brain pathologies. In particular, edema in white mattercan greatly alter signal levels, making it difficult to separate whitematter from other tissues.

In order to overcome this limitation, conventional methods detect thethicker cortical gray matter, and then use topological knowledge toestimate a location of an outer surface of the detected gray matter. Theproblem is thus transformed into finding a reliable segmentation of thecortical gray matter.

The cortex generally has a consistent thickness of about 3 mm. However,it can be difficult to differentiate gray matter from white matter. Thisdifficulty is often compounded by inhomogeneities in the RF fields,making it difficult or impossible to separate gray and white matter witha constant threshold value.

A common approach to tissue classification is to assign a label to eachvoxel, identifying it as white matter (WM), grey matter (GM), or CSF,based on the voxel's signal level. Threshold levels separating thetissues, for example, can be obtained from user input or estimated fromthe image histogram. Other methods may use different representations inan attempt to more accurately identify surfaces that separate the threecategories. Deformable surface models describe the interface either as aparameterized mesh of triangles (see, e.g., M. Kass, A. Witkin, and D.Terzopoulos, “Snakes: Active Contour Models,” Intl. J. Comp. Vision, v.1, pp. 312-333, 1988), or using level set methods, where a zero levelset implicitly defines the boundary (X. Zeng, L. H. Staib, R. T. Shultz,and J. S. Duncan, “Segmentation and Measurement of the Cortex from 3D MRImages Using Coupled Surfaces Propagation,” IEEE Trans. Med. Imaging, v.18, pp. 100-111, 1999). Deformable surface models can represent partialvoxels, but are generally complicated to implement and computationallyintensive to run.

Brain tissue classification methods that use MRI data as input canencounter difficulties. These difficulties include a variation in signalresponse, and structures that are smaller than the sampling rate. Theseissues are described below.

MRI data used for brain tissue classification often contain some amountof artifact or distortion due to inhomogeneities in the radio frequency(RF) fields and magnetic susceptibility variations in the tissue beingimaged. These effects give rise to small variations in the signalintensity that depend on the position of the sample location. Thesetypes of distortions are usually modeled as a multiplicativeslowly-varying ‘gain’ field that modifies the expected signal values,e.g., M(x, y, z)=G(x, y, z)×I(x, y, z), where M denotes the measuredsignal, I is the undistorted signal, G represents the gain field, and“x” denotes an element-wise product. G is a low frequency field withvalues near unity. The intensity normalization step attempts to estimatethe gain field and use it to restore the undistorted image.

A simple filtering method is often used to estimate G. Athree-dimensional low-pass filter with a wide window can be applied tothe image. Mean, median, and Gaussian filters have been used for thispurpose. The low-pass result can be used to estimate the gain field.

The normalization problem is sometimes converted into the estimation ofan additive ‘bias’ field B=log(G) by taking the logarithm of theexpression. W. M. Wells, W. E. Grimson, R. Kikinis, F. A. Jolesz,“Adaptive Segmentation of MRI Data,” IEEE Trans. Med. Imaging, v. 15,pp. 429-442, 1996 suggested this approach, and an expectationmaximization approach to solve the problem. In this method,normalization and tissue classification are combined. A statisticalclassification is performed, followed by bias field estimation using theclassification results, and then the estimated bias is removed. Thesesteps are iterated to converge on a normalized classification. Manyvariations of this strategy have been proposed. The difficulties withthis method are that they are complicated to implement and relativelytime-consuming to perform.

The second difficulty, structures too small to be detected at MRIresolution, occurs frequently at the cortical surface. Sufficientlylarge CSF-filled structures in the brain are usually easy to distinguishby their signal level alone. But the outer surface of the cortex, aGM-CSF boundary, has a complex geometry in which the CSF regions can beobscured. The topology is that of a highly folded sheet of gray matterabout three millimeters thick. The CSF-filled space between the innerfolds (sulci) may be very thin relative to the MRI resolution and thusmay not always directly visible in typical MRI data. A sulcus may appearin MRI as a continuous region of gray matter.

Knowledge of the brain's topology is often used in cortical surfacefinding algorithms in order to locate thin sulci that are not directlyvisible. The inner cortical GM-WM boundary is more easily located, asthe white matter structures inside the gyri are usually thick enough tobe detected. Therefore, one can locate the cortical GM-WM boundary, andthen use this information to help locate the GM-CSF boundary. X. Zeng,L. H. Staib, R. T. Shultz, and J. S. Duncan, “Segmentation andMeasurement of the Cortex from 3D MR Images Using Coupled SurfacesPropagation,” IEEE Trans. Med. Imaging, v. 18, pp. 100-111, 1999,developed a level set method for segmenting the cortex thatsimultaneously evolves a pair of surfaces, one seeking the WM-GMcortical boundary and the other seeking the outer GM-CSF corticalsurface. An additional constraint forces the two implicit surfaces toremain about three millimeters apart.

The coupled boundary level set approach has difficulty representing apair of GM-CSF boundaries that are separated by less than the voxelresolution. X. Han, C. Xu, D. Tosun, and J. Prince, “Cortical SurfaceReconstruction Using a Topology Preserving Geometric Deformable Model,”5th IEEE Workshop on Math. Methods in Bio. Image Anal., Kauai, Hi., pp.213-220, December 2001 propose a modified topology-preserving level setmethod to find the WM-GM cortical boundary only. They then compute the‘skeleton’ of the cortical gray matter, which locates regions that aremaximally distant from the WM-GM boundary on the GM side. This methodlocates the central plane within sulci well. The skeleton regions arethen marked as cortical surface. This method performs well at locatingthe cortical surface, but the level set approach for locating the GM-WMboundary is compute-intensive, considering that the WM-GM interface isamenable to simpler classification methods, such as thresholding.

SUMMARY OF THE INVENTION

The present invention provides a medical image processing system andmethod for classifying, preferably automatically, major tissue types,such as white matter (WM), gray matter (GM) and cerebro-spinal fluid(CSF). In a preferred embodiment, the system and method, data from twoor more similar volume data sets is used, the volume data sets beingsimilar except that at least one acquisition parameter has been variedto obtain different tissue contrast in the different volume data sets.The volume data sets preferably are generated by magnetic resonanceimaging (MRI), contrast is improved and/or inhomogeneities are reducedby combining the two similar, yet parameter varied, data sets, as bytaking a ratio of the data sets. The selection of acquisition parametersand combining methodologies are herein described.

The classification method may be further optimized by adjusting thevolume signal levels so that the classification boundaries tend to lieon edges in the original image.

As discussed herein, CSF boundaries in brain tissue can be identifiedfrom medical images which, for example, can be used to identify edema.The preferred imaging modality is magnetic resonance imaging, but anymodality with sufficient WM/GM/CSF contrast and resolution fine enoughto represent the cortex (a few millimeters) will suffice for use inclassifying different brain tissues and identifying boundaries. Voxelsthat primarily contain CSF (within the ventricles, surgical resectioncavities, and some sulci) may be identified, and voxels containing thecortical surface, even when the voxel's volume primarily contains graymatter (thin sulci), may be identified.

RF inhomogeneities can be minimized by using at least two related imagesequences in order to estimate and remove a bias term arising from theinhomogeneity. Multiple imaging modalities can be employed in order toidentify white matter, even in the presence of pathological conditions.MR diffusion tensor imaging may be used, as it is particularly sensitiveto variations in cell topology and is thus preferred for this task.

Preferably, medical images are used that show contrast between the whitematter, gray matter, and CSF. In previous methods, this usually is takento be a single three-dimensional MRI volume. Advantage is gained byusing two similar MRI volumes with preferably only a single, butoptionally two or more varied acquisition parameters to obtain differentWM/GM/CSF contrast levels. It may be assumed that the gain field arisingfrom magnetic susceptibility and RF field inhomogeneity will be similarin the two volumes. By taking the ratio of the two volumes, the commongain factor can be eliminated. Furthermore, by carefully selecting theacquisition parameters, the WM/GM/CSF contrast can be increased withrespect to the individual volumes. This methodology can improve theperformance of the remainder of the overall classifying method, but isnot required. Some low-frequency distortion may remain, but it can beaddressed through further processing as described below.

In particular, WM, GM and CSF can be classified by thresholding.Remaining low-frequency bias can be detected and normalized in this stepalso. Iterative normalization and classification algorithms generallylook at the sets of values of each type within a region of the volume,and base the gain estimation on these sets; see W. M. Wells, W. E.Grimson, R. Kikinis, F. A. Jolesz, “Adaptive Segmentation of MRI Data,”IEEE Trans. Med. Imaging, v. 15, pp. 429-442, 1996. Because the entireset of voxels is used in this procedure, such procedure can becomplicated and time-consuming. Thus, it is preferred to define thecurrent threshold and normalization by looking only at the WM/GM/CSFboundaries, and a gain correction may be selected that tends to placethe boundaries over edges in the image.

Thin sulcal CSF boundaries are often too narrow to be directly detectedin any common MR sequence. As described herein, cortical gray matter maybe detected (which is much more robust) and then a ‘skeletonizing’algorithm can be used to locate the thin sulcal boundaries centered in afold of cortical gray matter, similar to X. Han, C. Xu, D. Tosun, and J.Prince, “Cortical Surface Reconstruction Using a Topology PreservingGeometric Deformable Model,” 5th IEEE Workshop on Math. Methods in Bio.Image Anal., Kauai, Hi., pp. 213-220, December 2001. Thicker areas ofCSF will not be completely covered by the skeleton, but these areusually detected in the previous step by thresholding, since thick CSFusually is easy to distinguish.

BRIEF DESCRIPTION OF THE DRAWINGS

The forgoing and other embodiments of the invention are hereinafterdiscussed with reference to the drawings.

FIG. 1 is a graph showing relative signal strength vs. flip angle forideal SPGR at TR=20 ms for white matter and gray matter.

FIG. 2A-2C are SPRG slices of a brain.

FIG. 3 is a flow chart showing the steps of an exemplary method inaccordance with the invention.

FIG. 4 is an exemplary device which can be used in performing a methodin accordance with the invention.

DETAILED DESCRIPTION

A CSF boundary detection procedure includes the steps shown below.

-   -   1. Image pair pre-processing to enhance contrast and reduce bias        (optional).    -   2. WM/GM/CSF thresholded classification/inhomogeneity        correction.    -   3. Thin sulcus detection using the gray matter skeleton.        1. Image Pair Pre-Processing

Two MRI volumes can be acquired using identical acquisition parameters,varying, for example, one acquisition parameter. These two volumes canbe co-registered, if necessary, using known methods. Then the ratio ofthe two volumes is determined (any division by zero is replaced with thevalue zero). The ratio, for example, may be based on a voxel by voxel ordata point by data point calculation. Determining the ratio of the twovolumes has two advantages. First, intensity variations due to fieldinhomogeneities tend to be similar in both images, so the division tendsto normalize this undesirable effect such that it is insignificant.Second, when the acquisition parameters are chosen correctly, the GM/WMseparation can be enhanced compared to a single MR acquisition.

The preferred MRI sequence for brain tissue classification is a 3DSpoiled Gradient Recalled Echo (SPGR). The signal can be modeled byEquation 1 (ignoring noise and inhomogeneity):S=K*PD*(1−e ^(−TR/T1))sin(FA)/(1−(cos(FA)e ^(−TR/T1)))  Equation 1where T1 and PD depend upon the tissue properties, while the repetitiontime (TR) and flip angle (FA) are controlled parameters of theacquisition. Variable K incorporates several constant factors, includingfunctions of the echo time (TE) and the tissue T2. In the SPGR volumes,the tissue T1 and proton density (PD) both contribute significantly tothe signal levels. For flip angles above the Ernst angle (angle thatmaximizes the signal), the lower T1 of white matter typically accountsfor a 30-40% elevation of the white matter signal relative to graymatter. However, the PD of white matter is about 88% of gray matter, sothe relative WM signal is about 14-23% higher than GM. Below the Ernstangle, images are PD-weighted, and relatively insensitive to WM/GM T1variations. This can be seen in the graph of FIG. 1.

In this method, two SPGR volumes are acquired: A, with a flip angleabove the Ernst angle, and B, with a flip angle below the Ernst anglefor the range of T1 of WM and GM tissues. Then the ratio of the twovolumes, A/B can be computed. In this volume, the PD weighting may beremoved, and the signal variation between WM and GM is due mainly to theT1 differences, e.g., 30-40%.

FIGS. 2A-2C compare a slice of the low flip angle SPGR, the high flipangle SPGR, and their ratio. The ratio displays a strong separationbetween white and gray matter, and the CSF, where thick enough, also isseparated from the gray value. The typical intensity inhomogeneity iseasy to observe in the 25 degree flip angle image of FIG. 2A, in whichthe intensities are skewed lower in the middle of the image and brighternear the outer edges.

Gray-white contrast enhancement can be obtained using spin echo basedT1-weighted sequences also, by varying the repetition time, TR, tomodify the T1-weighting. Spin echo signal response is generally modeledas Equation 2:S=K*PD*(1−e ^(−TR/T1))  Equation 2again lumping all of the constant factors into K. By selecting one TRnear the tissue T1, and a second TR several times larger than theaverage T1, we obtain T1-weighted and PD weighted volumes with responsesimilar to that shown in FIG. 2. Their ratio gives a similarly enhancedgray-white contrast. Spin-echo sequences are far less prone to theinhomogeneities inherent in SPGR. However, if acquired using surfacecoils, the variability in the coil's RF reception can producesignificant low frequency variations that are normalized by this method.2. WM/GM/CSF Thresholded Classification/Inhomogeneity Correction

The gray/white threshold can be set by asking a user to select a set ofsample points in a user interface, e.g., to ‘paint’ a region by modelinga set of values underlying the selected gray or white matter region as aGaussian distribution, it is easy to extract a mean and standarddeviation for that region. This input should be accepted for WM, GM, andCSF. From white and gray values, a WM-GM threshold can be calculated asthe point at which the two Gaussian distribution curves intersect (e.g.,the value at which a point is equally likely to be white or graymatter). A GM-CSF threshold can be computed in the same manner.

The initial threshold value can be a first approximation to the optimalthreshold value, but it may not be ideal. Furthermore, even afternormalization, there may be some remaining variation in WM/GM/CSF meanintensities across the image.

A low frequency gain field G can be estimated to correct thesevariations. The gain volume can be at a much coarser resolution than theimage. Approximately 1 cm resolution is appropriate for G, about afactor of ten coarser than the image volume.

Prior methods estimate the gain from the thresholded image by looking atthe statistics of the sets of voxels in each of the three categories;see W. M. Wells, W. E. Grimson, R. Kikinis, F. A. Jolesz, “AdaptiveSegmentation of MRI Data,” IEEE Trans. Med. Imaging, v. 15, pp. 429-442,1996. Gain adjustments that center the statistics of local sub-regionstowards the global value are specified. As described herein, and incontrast to the prior art, focus may be placed on the alignment of theboundaries between the sub-regions. The boundaries in the originalvolume can be detected by any standard three-dimensional edge detectionoperation. In the preferred implementation, the magnitude of thegradient of the image volume can be computed. A classification volumecan be formed by assigning integers 0, 1, 2 to CSF, GM, and WM regionsdetermined by thresholding. An edge detection of the classificationvolume then can be computed. A measure of the quality of the match ofthe boundaries of the image and classification volumes then can bedefined. The mutual information measure is well suited for this purpose(see P. Viola and W. M. Wells III, “Alignment by Maximization of MutualInformation,” in Intl. Conf. on Comp. Vision, E. Grimson, S. Shafer, A.Blake, and K. Sugihara, Eds., IEEE Computer Society Press, Los Alamitos,Calif., pp. 16-23, 1995 for the computation of the mutual informationfor volume registration). Simpler measures are also acceptable, such asa normalized sum of the product of the two volumes, as shown in Equation3:

$\begin{matrix}\frac{\sum\limits_{x,y,z}\;{i_{x,y,z} \cdot c_{x,y,z}}}{\sum\limits_{x,y,z}\; c_{x,y,z}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

Symbol i denotes the value of voxel position (x, y, z) of theedge-detected image volume, normalized by the current gain, and cdenotes the value of the edge-detected classification volume.

The gain field can be adjusted to maximize the mutual information orother similarity measure. Gradient descent is suitable for thisoptimization procedure, since the starting point is generally close tothe optimal. Furthermore, the change in the measure can be computed farmore rapidly than the measure itself, since the effect of one gain fieldvoxel is limited to a small sub-volume of the image field.

3. Thin Sulcus Detection

Classification will adequately identify the WM/GM/CSF regions that arelarge relative to the image resolution. The method proposed by X. Han,C. Xu, D. Tosun, and J. Prince, “Cortical Surface Reconstruction Using aTopology Preserving Geometric Deformable Model,” 5th IEEE Workshop onMath. Methods in Bio. Image Anal., Kauai, Hi., pp. 213-220, December2001, can be used at this step to locate cortical surfaces in thin sulciusing a skeleton computation. The GM skeleton is the set of points ofmaximal distance from the WM-GM boundary, which tends to be preciselythe location of a thin cortical surface in the sulcal fold of corticalgray. First, a signed distance map from the WM-GM boundary can becomputed. The Laplacian of the distance map can be computed, andLaplacian values above a fixed threshold can be classified as corticalsurface.

The method may lack the sub-pixel accuracy of the level set approach,but is simpler and faster. Preferably all CSF-containing voxels aremarked, and the sub-pixel accuracy is unnecessary in this application. Arefinement to this method is to limit the maximum distance that theskeleton is allowed to traverse. If it is assumed that the corticalGM-CSF surface is always within some maximum distance (severalmillimeters) of the cortical WM-GM boundary, then points at greaterdistance are not considered. This eliminates many false positiveskeleton values from deep brain gray matter structures.

FIG. 3 shows the steps of an exemplary method in accordance with theinvention.

Different imaging modalities have different advantages anddisadvantages. For example, images acquired with MRI (Magnetic ResonanceImaging) grey and white matter of brain tissue can easily bedistinguished, but spatial resolution and spatial accuracy is worsecompared to images acquired with CT (Computer Tomography). It is quitedifficult, however, to distinguish between grey and white matter in CTimages.

Usually in T1-weighted (T1w) MR images a sufficient spatial resolutioncan be reached, but water, especially edema, can hardly be seen.T2-weighted (T2w) images can be used to identify regions of CNF(Cerebro-Spinal Fluid). A very sufficient imaging dataset to identifyedema can be calculated from diffusion weighted (DWI) MR images or fromthe diffusion tensor (DTI) using the fact that the anisotropy in themovement of water molecules will decrease in edema regions. However, DWIimages currently suffer from a low spatial resolution and accuracy.

For a highly reliable segmentation of brain surfaces, the advantagesfrom the various imaging modalities can be combined. For example, if T1wand T2w data sets are morphed and fused to the CT data, the T2w datasetcan be used for detection of outer surfaces and the T1w dataset can beused to distinguish between white and grey matter. From those datasets anew dataset (DS1) can be created showing the boundaries between CSF andwhite and grey matter. If the DWI dataset is fused and morphed to the CTdataset, a new (DWI1) dataset is created showing diffusion related datain an anatomical correct environment. DWI1 can be used to identifyedema. This information can be fused to DS1 creating a new dataset DS2that contains anatomically correct information about boundaries betweengrey and white matter, brain surfaces and edema. In the same manner moredatasets containing more information can be used to identify morerequired regions.

Furthermore, datasets can be used to correct for image inhomogenietiesor other imaging errors. For example, distortions from phase errors canbe corrected using field maps, and the corrected images can be used tocorrect the other data sets.

FIG. 4 is a block diagram of a system 10 for implementing one or more ofthe methods described herein. The system 10 includes a computer 12 forprocessing data, and a display 14 for viewing system information. Akeyboard 16 and pointing device 18 may be used for data entry, datadisplay, screen navigation, etc. The keyboard 16 and pointing device 18may be separate from the computer 12 or they may be integral to it. Acomputer mouse or other device that points to or otherwise identifies alocation, action, etc., e.g., by a point and click method or some othermethod, are examples of a pointing device. Alternatively, a touch screen(not shown) may be used in place of the keyboard 16 and pointing device18. A touch screen is well known by those skilled in the art and willnot be described herein.

Included in the computer 12 is a storage medium 20 for storinginformation, such as application data, screen information, programs,etc. The storage medium 20 may be a hard drive, for example. A processor22, such as an AMD Athlon 64® processor or an Intel Pentium IV®processor, combined with a memory 24 and the storage medium 20 executeprograms to perform various functions, such as data entry, numericalcalculations, screen display, system setup, etc. A network interfacecard (NIC) 26 allows the computer 22 to communicate with devicesexternal to the system 10.

Communicatively coupled to the computer 12 is a first imaging system 30(e.g., a CT imaging system) and a second imaging system 32 b (e.g., anMRI system). As will be appreciated, other imaging systems may beutilized in place of the CT and MRI systems. The first and secondimaging systems can provide imaging data to the computer 12, which usesthe data in accordance with the method described herein.

The actual code for performing the functions described herein can bereadily programmed by a person having ordinary skill in the art ofcomputer programming in any of a number of conventional programminglanguages based on the disclosure herein. Consequently, further detailas to the particular code itself has been omitted for sake of brevity.

Although the invention has been shown and described with respect to acertain preferred embodiment or embodiments, it is obvious thatequivalent alterations and modifications will occur to others skilled inthe art upon the reading and understanding of this specification and theannexed drawings. In particular regard to the various functionsperformed by the above described elements (components, assemblies,devices, compositions, etc.), the terms (including a reference to a“means”) used to describe such elements are intended to correspond,unless otherwise indicated, to any element which performs the specifiedfunction of the described element (i.e., that is functionallyequivalent), even though not structurally equivalent to the disclosedstructure which performs the function in the herein illustratedexemplary embodiment or embodiments of the invention. In addition, whilea particular feature of the invention may have been described above withrespect to only one or more of several illustrated embodiments, suchfeature may be combined with one or more other features of the otherembodiments, as may be desired and advantageous for any given orparticular application.

What is claimed is:
 1. A computer-implemented medical imaging processingmethod comprising executing, on a processor of a computer, steps of:acquiring, at the processor, at least first and second data sets of aregion of interest of a patient's body by imaging the patient's body,with at least one image acquisition parameter being changed, by theprocessor, so that the first and second data sets yield differentcontrast levels relating to different tissue types, and with at leastone image acquisition parameter used to obtain the first data set beingselected, by the processor, to enhance the contrast between one of thetissue types relative to other tissue types, and with the at least oneimage acquisition parameter used to obtain the second data set beingselected, by the processor, to enhance the contrast between a differentone of the tissue types relative other tissue types, thereby to optimizethe contrast between at least three different tissue types; processing,by the processor, at least the first and second data sets to identifythe different tissue types and/or boundaries therebetween, saidprocessing including: forming, by the processor, a combined data set bycombining the at least first and second data sets; and calculating, bythe processor, a gain field to correct for variations in meanintensities across the combined data set, wherein the gain field isadjusted, by the processor, to maximize a similarity measure betweendetected boundaries of sub-regions in the combined data set and detectedboundaries of a classification volume derived from the combined dataset; and using, by the processor, a skeletonizing algorithm on at leastone of the identified tissue types or boundaries to locate sulci.
 2. Themethod according to claim 1, wherein the first and second data sets areobtained by magnetic resonance imaging.
 3. The method according to claim1, wherein a single image acquisition parameter is changed, by theprocessor, when obtaining the first and second data sets.
 4. The methodaccording to claim 3, wherein the single image acquisition parameter ischanged, by the processor, so as to enhance T1 contrast.
 5. The methodaccording to claim 1, wherein the at least one image acquisitionparameter is a flip angle.
 6. The method according to claim 5, whereinthe flip angle is set above and below the Ernst angle for the first andsecond data sets, respectively.
 7. The method according to claim 1,further comprising varying, by the processor, the at least one imageacquisition parameter to optimize the data acquisition with respect tothe distinction of white matter and grey matter in a first data set. 8.The method according to claim 1, further comprising varying, by theprocessor, the at least one image acquisition parameter to optimize dataacquisition with respect to the distinction of white matter andcerebrospinal fluid filled regions in a second data set.
 9. The methodaccording to claim 1, further comprising classifying, by the processor,white matter, gray matter and cerebrospinal fluid filled regions in theclassification volume by thresholding.
 10. The method according to claim1, further comprising calculating, by the processor, the boundaries forwhite mater, grey matter and/or cerebrospinal fluid from the at leastfirst and second data sets.
 11. The method according to claim 1, furthercomprising including, by the processor, assumptions, estimates or apredefined atlas about anatomical structures and/or physiologicalproperties of one or more volume elements in the classification volumebased on an assignment using image data.
 12. The method according toclaim 1, further comprising using, by the processor, a diffusion tensorMRI to acquire the first and/or second data sets.
 13. The methodaccording to claim 1, wherein the first and/or second data sets comprisean MRI image with a spatial resolution of at least one by one by threemillimeters.
 14. The method according to claim 1, wherein the imagingincludes using MRI sequences with varying flip angles.
 15. The methodaccording to claim 1, wherein using the skeletonizing algorithm includesdefining, by the processor, a gray-matter skeleton as a set of pointswithin a predetermined distance from a white matter-gray matterboundary.
 16. The method according to claim 1, further comprisingadjusting, by the processor, signal levels for the first and second datasets so that boundaries lie on edges in the pre-processed first andsecond data sets.
 17. A computer program embodied on a non-transitorycomputer-readable medium for computer-implemented medical imagingprocessing, comprising: code that directs imaging of a patient's body toacquire, at a processor of a computer, at least first and second datasets of a region of interest of the patient's body, with at least oneimage acquisition parameter being changed, by the processor, so thatfirst and second data sets yield different contrast levels relating todifferent tissue types, and wherein the at least one image acquisitionparameter used to obtain the first data set is selected, by theprocessor, to enhance the contrast between one of the tissue typesrelative to other tissue types, and the at least one image acquisitionparameter used to obtain the second data set is selected, by theprocessor, to enhance the contrast between a different one of the tissuetypes relative other tissue types, thereby to optimize the contrastbetween at least three different tissue types; code for processing, bythe processor, the at least first and second data sets to identify thedifferent tissue types and/or boundaries therebetween, said processingincluding forming, by the processor, a combined data set by combiningthe at least first and second data sets; and calculating, by theprocessor, a gain field to correct for variations in mean intensitiesacross the combined data set, wherein the gain field is adjusted, by theprocessor, to maximize a similarity measure between detected boundariesof sub-regions in the combined data set and detected boundaries of aclassification volume derived from the combined data set; and code forimplementing, by the processor, a skeletonizing algorithm on at leastone of the identified tissue types or boundaries to locate sulci. 18.The computer program according to claim 17, wherein the code forimplementing the skeletonizing algorithm includes code for defining, bythe processor, a gray-matter skeleton as a set of points within apredetermined distance from a white matter-gray matter boundary.
 19. Thecomputer program according to claim 17, further comprising code foradjusting, by the processor, volume signal levels so that boundaries lieon edges in the first and second data sets.
 20. An apparatus for medicalimage processing, comprising: at least one imaging or data acquisitionsystem; a processor and memory; and image acquisition and processinglogic stored in the memory and executable by the processor, wherein theimage acquisition and processing logic includes: logic for directingimaging of a patient's body to acquire, at the processor, at least firstand second data sets of a region of interest of the patient's body, withat least one image acquisition parameter being changed, by theprocessor, so that first and second data sets yield different contrastlevels relating to different tissue types, and wherein the at least oneimage acquisition parameter used to obtain the first data set isselected, by the processor, to enhance the contrast between one of thetissue types relative to other tissue types, and the at least one imageacquisition parameter used to obtain the second data set is selected, bythe processor, to enhance the contrast between a different one of thetissue types relative other tissue types, thereby to optimize thecontrast between at least three different tissue types; logic forprocessing, by the processor, the at least first and second data sets toidentify the different tissue types and/or boundaries therebetween, saidprocessing including: forming a combined data set by combining the atleast first and second data sets; and calculating, by the processor, again field to correct for variations in mean intensities across thecombined data set, wherein the gain field is adjusted, by the processor,to maximize a similarity measure between detected boundaries ofsub-regions in the combined data set and detected boundaries of aclassification volume derived from the combined data set; and logic forimplementing, by the processor a skeletonizing algorithm on at least oneof the identified tissue types or boundaries to locate sulci.
 21. Theapparatus according to claim 20, wherein the logic for implementing theskeletonizing algorithm includes logic for defining, by the processor, agray-matter skeleton as a set of points within a predetermined distancefrom a white matter-gray matter boundary.
 22. The apparatus according toclaim 20, further comprising logic for adjusting, by the processor,volume signal levels so that boundaries lie on edges in the first andsecond data sets.
 23. A computer-implemented medical imaging processingmethod comprising executing, on a processor of a computer, steps of:obtaining, at the processor, at least first and second data sets of aregion of interest of a patient's body that have been produced by animaging method for imaging the patient's body with at least one imageacquisition parameter being different such that first and second datasets yield different contrast levels relating to different tissue types,and wherein the at least one image acquisition parameter used to obtainthe first data set enhances the contrast between one of the tissue typesrelative to other tissue types, and the at least one image acquisitionparameter used to obtain the second data set enhance the contrastbetween a different one of the tissue types relative other tissue types;processing, by the processor, the at least first and second data sets toidentify the different tissue types and/or boundaries therebetween, saidprocessing including: forming, by the processor, a combined data set bycombining the at least first and second data sets; and calculating, bythe processor, a gain field to correct for variations in meanintensities across the combined data set, wherein the gain field isadjusted, by the processor, to maximize a similarity measure betweendetected boundaries of sub-regions in the combined data set and detectedboundaries of a classification volume derived from the combined dataset; using, by the processor, a skeletonizing algorithm on at least oneof the identified tissue types or boundaries to locate sulci; andoutputting, by the processor, the location of the sulci for review. 24.The method according to claim 23, wherein using a skeletonizingalgorithm includes defining, by the processor, a gray-matter skeleton asa set of points within a predetermined distance from a white matter-graymatter boundary.
 25. The method according to claim 23, furthercomprising adjusting, by the processor, volume signal levels so thatboundaries lie on edges in the first and second data sets.